This document forms part of the data and code deposited at:
https://github.com/acp29/Elmasri_GRIN2B

Load package requirements

if (!require(package="tidyverse")) utils::install.packages("tidyverse")
library(tidyverse) 
if (!require(package="lme4")) utils::install.packages("lme4")
library(lme4)  
if (!require(package="HLMdiag")) utils::install.packages("HLMdiag")
library(HLMdiag) 
if (!require(package="parameters")) utils::install.packages("parameters")
library(parameters) 
if (!require(package="car")) utils::install.packages("car")
library(car)  
if (!require(package="performance")) utils::install.packages("performance")
library(performance)  
if (!require(package="BayesFactor")) utils::install.packages("BayesFactor")
library(BayesFactor) 
if (!require(package="bayestestR")) utils::install.packages("bayestestR")
library(bayestestR) 
if (!require(package="stats")) utils::install.packages("stats")
library(stats)
if (!require(package="pCalibrate")) utils::install.packages("pCalibrate")
library(pCalibrate)
if (!require(package="afex")) utils::install.packages("afex")
library(afex)
if (!require(package="emmeans")) utils::install.packages("emmeans")
library(emmeans)
if (!require(package="multcomp")) utils::install.packages("multcomp")
library(multcomp)
if (!require(package="knitr")) utils::install.packages("knitr")
library(knitr)
if (!require(package="kableExtra")) utils::install.packages("kableExtra")
library(kableExtra)
if (!require(package="ggplot2")) utils::install.packages("ggplot2")
library(ggplot2)
if (!require(package="qqplotr")) utils::install.packages("qqplotr")
library(qqplotr)
if (!require(package="gridExtra")) utils::install.packages("gridExtra")
library(gridExtra)
if (!require(package="ggforce")) utils::install.packages("ggforce")
library(ggforce)
if (!require(package="devEMF")) utils::install.packages("devEMF")
library(devEMF)
if (!require(package="effectsize")) utils::install.packages("effectsize")
library(effectsize)

Read text in from file

Data <- read.delim("../data/n2b_mutant.dat", header = TRUE)
# Implicit nesting (required for anovaBF)
Data %>% 
  mutate(mutation = as.factor(mutation)) %>% 
  mutate(animal = paste0(as.numeric(mutation),animal)) %>% 
  mutate(slice = paste0(animal,slice)) %>% 
  mutate(pair = paste0(slice,pair)) %>% 
  mutate(pair = factor(pair)) -> Data

Factor encoding

Data$mutation <- as.factor(Data$mutation)
Data$transfection <- as.factor(Data$transfection)
Data$animal <- as.factor(Data$animal)
Data$slice <- as.factor(Data$slice)
Data$pair <- as.factor(Data$pair)

Set mutation WT and transfection - as reference levels

Data$mutation <- factor(Data$mutation, levels=c("WT","R540H","R696H","C456Y","C461F"))
Data$transfection <- factor(Data$transfection, levels=c("-","+"))

lmer settings

settings <- lmerControl(check.conv.singular = .makeCC(action = "ignore",  tol = 1e-4), boundary.tol=0)

Fit a mixed linear model

# Initialize
variates <- c("peaknmda","decaynmda","dt50nmda","chargenmda","peakampa","decayampa","dt50ampa","chargeampa")
l <- length(variates)

for (i in 1:l) {
  
variates[i] -> resp  

cat('\n\n\n# Analysis of',resp,'\n\n')

# Plot data
# colours selected from:
#  > library(scales)
#  > show_col(hue_pal()(9))
p1 <- Data %>%
    mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
           grouping=interaction(pair, mutation)) %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
    ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) + 
    geom_blank() +
    geom_line(aes(mutation_jittered), alpha=0.2, color="grey") +
    geom_point(aes(mutation_jittered), alpha=0.4, shape=16) +
    scale_color_manual(values=c("grey","#00BA38")) +
    stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0,inherit.aes=FALSE) + 
    stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1, inherit.aes=FALSE) +
    ylab(resp) +
    ggtitle("a") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.title = element_blank(),
          legend.position = "top")
p2 <- Data %>% 
    pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>% 
    mutate(ratio = `+`/`-`) %>%
    ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
    geom_sina(alpha=0.9, shape = 16) + 
    scale_color_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
    stat_summary(fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0) + 
    stat_summary(fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1) +
    ylab("ratio") +
    ggtitle("b") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.position = "none")
grid.arrange(p1, p2, nrow = 1, ncol = 2, top=sprintf("Summary plots of the data for: %s\n",resp))

# Fit the model with planned contrasts and perform hypothesis testing
  
# Setup planned, orthogonal contrasts
# ("WT","R540H","R696H","C456Y","C461F")
# According to features of the mutations or experiments in heterologous expression systems
WT_vs_Mutants <- c(-4,1,1,1,1)/5
LOF_vs_GOF    <- c(0,2,2,-2,-2)/4
LOF           <- c(0,0,0,-1,1)/2
GOF           <- c(0,-1,1,0,0)/2
contr.orth <- cbind(WT_vs_Mutants, LOF_vs_GOF, LOF, GOF)
rownames(contr.orth) <- levels(Data$mutation)

# Check that contrasts are indeed orthogonal
contr.orth %>%
  cor() %>%
  knitr::kable(caption = sprintf("**All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()
contr.orth %>%
  colSums() %>%
  as.data.frame() %>%
  rename(.,'sum' = '.') %>%
  knitr::kable(caption = sprintf("**Sum of each orthogonal contrast should be zero: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Fit model (with orthogonal contrasts)
contrasts(Data$mutation) <- contr.orth
attr(Data$mutation,"contrasts") %>%
  as.data.frame() %>%
  rownames_to_column(var = "mutation") %>% 
  knitr::kable(caption = sprintf("**Matrix of contrasts on mutation: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()
contrasts(Data$transfection) <- rbind(-1,1)/2
attr(Data$transfection,"contrasts") %>% 
  as.data.frame() %>%
  rename(contrast = "V1") %>%
  rownames_to_column(var = "transfection") %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+")  %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-")  %>% 
  knitr::kable(caption = sprintf("**Matrix of contrasts on transfection: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print() 
formula <- sprintf("log(%s) ~ mutation * transfection + (1|animal/slice/pair)", resp)
model <- lme4::lmer(formula, data = Data, REML = TRUE, control = settings, na.action = "na.fail") 

# Checking model assumptions
resid = residuals(model)
n = length(resid)
stdev = sqrt((n-1)/n) * sd(resid) # standard deviation with denominator n
std_resid = resid/stdev
p1 <- ggplot(Data, aes(x = fitted(model), y = std_resid)) +
  geom_point() +
  ggtitle("a") +
  xlab("Fitted values") + ylab("Standardized Residuals") +
  geom_hline(yintercept = 0) +
  geom_quantile(formula=y~x, color="#619CFF", size=1) +
  geom_smooth(method="loess", formula = y ~ x, color="#F8766D", size=1, se=FALSE)
p2 <- ggplot(Data, aes(x = std_resid)) +
  geom_histogram(aes(y=..density..), binwidth = 0.9*n^(-1/5), fill="#619CFF", alpha=0.33)  +
  geom_density(kernel="gaussian", alpha=0, color="#619CFF", size=1) +
  ggtitle("b") +
  xlab("Standardized Residuals") + ylab("Density") +
  geom_vline(xintercept = 0) +
  geom_function(fun = dnorm, args = list(mean=0, sd=1), col = "#F8766D", size = 1)
p3 <- ggplot(Data, aes(sample = std_resid)) +
  geom_qq_band(distribution = "norm", bandType = "ts", mapping = aes(fill = "TS"), fill="#619CFF", alpha = 0.33) +
  stat_qq() + 
  stat_qq_line(color="#F8766D",size=1) +
  ggtitle("c") +
  xlab("Normal Quantiles") + ylab("Sample Quantiles") 
infl <- hlm_influence(model, level="pair:(slice:animal)")
p4 <- infl %>% 
  mutate(influential = cooksd > 1.0) %>% 
  ggplot(aes(x=`pair:(slice:animal)`,y=cooksd, color=influential)) + 
  geom_segment(aes(x=`pair:(slice:animal)`, xend=`pair:(slice:animal)`, y=0, yend=cooksd)) + 
  geom_point() + 
  ylab("Cook's distance") +
  scale_color_manual(values=c("#619CFF","#F8766D")) + 
  ggtitle("d") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "none",
        panel.background = element_rect(color="#EBEBEB"),
        panel.grid = element_blank(),
        panel.grid.minor.y = element_line(color = "white", size=0.25),
        panel.grid.major.y = element_line(color = "white", size=0.5),
        axis.line = element_blank(),
        axis.line.x = element_line(size = 0.5, colour = "black"))
grid.arrange(p1, p2, p3, p4, nrow=2, ncol=2, top=sprintf("Plots of standardized model residuals and Cook's distances: %s\n",resp))

# Calculate ANOVA table for the fitted model (Type III sum of squares) 
car::Anova(model, type = 3, test.statistic = "F") %>%       # Uses Kenward-Roger degrees of freedom
  as.data.frame() %>%
  rownames_to_column(var="Source") %>%
  filter(Source != "(Intercept)") -> aov

# Calculate Bayes Factors for ANOVA and append them to the ANOVA data frame
# Inclusion Bayes Factor based on matched models (prior odds uniform-equal)
Data %>% 
  mutate(logresp = log(!!sym(resp))) %>%
  as.data.frame() -> Data
set.seed(123456)
anovaBF(logresp ~ mutation * transfection + animal + slice + pair, 
                 whichRandom = c("animal","slice","pair"), 
                 whichModels = "withmain", 
                 iterations = 20000,
                 data = Data) %>%
  bayesfactor_inclusion(match_models = TRUE) %>% 
  as.data.frame() %>% 
  na.omit() %>%    # removes the (nuisance) random factors
  mutate(BF = exp(log_BF)) %>%
  mutate_at("BF", formatC, format='g',digits = 3) %>% 
  dplyr::select(BF) %>% 
  unlist() -> aov$BF

# Calculate orthogonal contrasts and append them to the ANOVA summary table
# I go to the trouble of transforming the t-statistic (which is returned from the linear model) 
# to an F statistic but they give identical p-values; I think this makes more sense and provides 
# more consistency when splitting the source of variation into orthogonal contrasts and presenting 
# them in an ANOVA table (eg. like with summary.aov or orthogonal contrasts in SAS)
model_parameters(model, ci_method = "kenward", exponentiate = TRUE, effects = "fixed") %>%  
  filter(grepl(":",Parameter)) %>%                          # select interaction terms only
  rename(Source = Parameter) %>%                            # set denominator degrees of freedom
  mutate(Df = 1) %>%                                        # set numerator degrees of freedom
  add_column(BF = "")  %>%                                  # add empty column for Bayes factors
  rename(Df.res = df_error) %>%                             # set denominator degrees of freedom
  mutate(F = abs(t)^2) %>%                                  # calculate F statistic
  mutate(`Pr(>F)` = pf(F,Df,Df.res,lower.tail=FALSE)) %>%   # calculate p value
  dplyr::select(c(Source,F,Df,Df.res,`Pr(>F)`,BF)) %>%      # select columns of interest for table
  rbind(aov,.) %>%                                          # row bind with anova table
  mutate(`Pr(>F)` = afex::round_ps_apa(`Pr(>F)`)) %>%       # format p values as APA style
  knitr::kable(caption = sprintf("**ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  add_indent(c(4:7)) %>%                                    # add indentation to indicate source components
  print()

# Calculate intraclass correlation coefficients (ICC) for the random effects
icc(model, by_group=TRUE, tolerance=0) %>% 
  as.data.frame() %>% 
  mutate(N = ngrps(model)) %>%
  rbind(.,c("residual",1-sum(.$ICC),nobs(model))) %>%
  mutate(ICC = as.numeric(ICC)) %>%                               
  knitr::kable(caption = sprintf("**Intraclass correlation coefficients for random effects: %s**",resp), digits = 3) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Calculated estimated marginal means, By default, emmeans uses Kenward-Roger's method for estimating the degrees of freedom
emm <- emmeans(model, ~ mutation * transfection, data = Data, tran = 'log', type = 'response')
emm %>% 
  summary(calc = c(n = ".wgt.")) %>%
  as.data.frame() %>%
  mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+")  %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-")  %>% 
  relocate(df, .before = response) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Calculate overall average for untransfected neurons
emmeans(model, ~ mutation * transfection, data = Data) %>%
  as.data.frame() %>%
  filter(transfection == "-") %>%
  dplyr::select(emmean) %>% 
  colMeans() %>%
  exp() %>%
  sprintf("**Overall average of %s for untransfected neurons**: %.2f",resp,.) %>%
  print()

# Calculate transfected/untransfected ratios
emm.transfection <- contrast(emm, method = "trt.vs.ctrl", interaction = FALSE, by = 'mutation', adjust = "none")
emm.transfection %>%
  confint() %>%
  as.data.frame() %>%
  relocate(df, .before = ratio) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals for transfected/untransfected ratios: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# 95% confidence intervals for interaction contrasts
emm.interaction <- contrast(emm, method = "trt.vs.ctrl", interaction = TRUE, adjust = "none")
emm.interaction %>%
  confint() %>% 
  relocate(df, .before = ratio) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**95%% confidence intervals for contrasts: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Standardized effect sizes (*r*) for interaction contrasts
# Methods used the same as this server: https://easystats4u.shinyapps.io/statistic2effectsize/
emm.interaction %>% 
    as.data.frame() %>% 
    mutate(n = df+nrow(.)+1) %>% 
    mutate(r = t_to_r(t.ratio, df)$r) %>% 
    mutate(z = atanh(r),
           SE = 1/sqrt(n-3),
           CI = sprintf("[%.2f, %.2f]",
                        LL = tanh(z - 1.96*SE),
                        UL = tanh(z + 1.96*SE))) %>%
    dplyr::select(-c(ratio,SE,df,null,t.ratio,p.value,z)) %>%
    knitr::kable(col.names = c("mutation",
                               "transfection",
                               "*n*",
                               "*r*",
                               "95% *CI*"),
                 caption = sprintf("**Standardized effect sizes (*r*) for contrasts: %s**",resp), digits = 2) %>% 
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
    print()

posthoc = TRUE

if (posthoc == TRUE) {
  
# p-values and maximum Bayes Factors for interaction contrasts 
# Dunnett's step-down adjustment to control FWER on p-values (using multcomp package)
# Chapter 4.1.2 in Bretz, F., Hothorn, T. and Westfall, P. (2011) Multiple Comparisons Using R. Taylor and Frances Group, LLC.
emm.interaction %>%   
    as.glht() %>%
    summary(test = adjusted(type = "free")) -> glht.out  
emm.interaction %>%   
  as.data.frame() %>%
  dplyr::select(-SE) %>%
  mutate(p.adj = glht.out$test$pvalues) %>%
  mutate(p.adj = sapply(p.adj,max,.Machine$double.eps)) %>%
  mutate(maxBF = 1/pCalibrate(p.adj,"exploratory")) %>%
  mutate_at("maxBF", formatC, format='g',digits = 3) %>%
  mutate(p.value = afex::round_ps_apa(p.value)) %>%
  mutate(p.adj = afex::round_ps_apa(p.adj)) %>%
  knitr::kable(caption = sprintf("**Hypothesis testing on interaction parameters (Dunnett's step-down p-value adjustment): %s**",resp), digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

}
# Replot data with 95% confidence intervals
emf(sprintf("../img/%s_%s.emf","n2b_mutants",resp), width=4.5, height=3.5)
emm %>%
    as.data.frame() %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) -> emm_df
p1 <- Data %>%
    mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
           grouping=interaction(pair, mutation)) %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
    ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) + 
    geom_blank() +
    geom_line(aes(mutation_jittered), alpha=0.3, color="grey", size=0.75) +
    geom_point(aes(mutation_jittered), alpha=0.6, shape = 16, size=1.25) +
    scale_color_manual(values=c("grey","#00BA38")) +
    scale_fill_manual(values=c("grey","#00BA38")) +
    geom_crossbar(data = emm_df, 
                    aes(x=mutation_transfection, y=response, ymin=`lower.CL`, ymax=`upper.CL`, fill=transfection), 
                    color="black", alpha=0.5, size=0.5, fatten=1, width=0.3, inherit.aes=FALSE) + 
    ylab(resp) +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.title = element_blank(),
          legend.position = c(0.5, 1.06),
          legend.direction = "horizontal",
          text = element_text(size=14))
emm.transfection %>%
    confint() %>%
    as.data.frame() -> emm.transfection_df
p2 <- Data %>% 
    pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>% 
    mutate(ratio = `+`/`-`) %>%
    ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
    geom_sina(alpha=0.6, shape=16, size=1.25, maxwidth=0.5) + 
    geom_crossbar(data = emm.transfection_df, 
                           aes(x=mutation, y=ratio, ymin=`lower.CL`, ymax=`upper.CL`, fill=mutation), 
                    color="black", alpha=0.5, size=0.5, fatten=1, width=0.8, inherit.aes=FALSE) +
    scale_colour_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
    scale_fill_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
    ylab("ratio") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1), axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.position = "none",
          text=element_text(size=14))
grid.arrange(p1, p2, layout_matrix=rbind(c(1,2)), top=sprintf("Summary plots of the data with 95%% confidence intervals: %s\n",resp))
dev.off() #turn off device and finalize file


}

Analysis of peaknmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peaknmda
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: peaknmda
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: peaknmda
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: peaknmda
transfection contrast
- -0.5
+ 0.5
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peaknmda
Source F Df Df.res Pr(>F) BF
mutation 2.91 4 16.16 .055 2.02
transfection 51.00 1 91.00 <.001 1.22e+08
mutation:transfection 0.18 4 91.00 .947 0.0511
mutationWT_vs_Mutants:transfection1 0.43 1 91.00 .513
mutationLOF_vs_GOF:transfection1 0.28 1 91.00 .595
mutationLOF:transfection1 0.07 1 91.00 .796
mutationGOF:transfection1 0.00 1 91.00 .960
Intraclass correlation coefficients for random effects: peaknmda
Group ICC N
pair:(slice:animal) 0.147 96
slice:animal 0.311 62
animal 0.335 22
residual 0.207 192
Estimated marginal means with 95% confidence intervals: peaknmda
mutation transfection df response n lower.CL upper.CL
WT - 16.54 162.54 19 86.74 304.60
R540H - 17.48 92.40 27 54.27 157.31
R696H - 18.13 43.36 17 22.88 82.17
C456Y - 20.22 108.96 18 60.57 196.00
C461F - 16.32 71.66 15 34.80 147.57
WT + 16.54 118.04 19 62.99 221.21
R540H + 17.48 63.69 27 37.41 108.44
R696H + 18.13 29.65 17 15.64 56.18
C456Y + 20.22 71.74 18 39.88 129.05
C461F + 16.32 44.99 15 21.85 92.64
[1] “Overall average of peaknmda for untransfected neurons: 87.35”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peaknmda
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 91 0.73 0.57 0.92
(+) / (-) R540H 91 0.69 0.56 0.84
(+) / (-) R696H 91 0.68 0.53 0.88
(+) / (-) C456Y 91 0.66 0.51 0.84
(+) / (-) C461F 91 0.63 0.48 0.82
95% confidence intervals for contrasts: peaknmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 91 0.95 0.69 1.30
R696H / WT (+) / (-) 91 0.94 0.66 1.33
C456Y / WT (+) / (-) 91 0.91 0.64 1.28
C461F / WT (+) / (-) 91 0.86 0.60 1.24
Standardized effect sizes (r) for contrasts: peaknmda
mutation transfection n r 95% CI
R540H / WT (+) / (-) 96 -0.03 [-0.23, 0.17]
R696H / WT (+) / (-) 96 -0.04 [-0.23, 0.17]
C456Y / WT (+) / (-) 96 -0.06 [-0.26, 0.14]
C461F / WT (+) / (-) 96 -0.08 [-0.28, 0.12]
## Note: df set to 91
Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): peaknmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl ratio df null t.ratio p.value p.adj maxBF
R540H / WT (+) / (-) 0.95 91 1 -0.33 .742 .916 1
R696H / WT (+) / (-) 0.94 91 1 -0.34 .732 .916 1
C456Y / WT (+) / (-) 0.91 91 1 -0.57 .573 .894 1
C461F / WT (+) / (-) 0.86 91 1 -0.80 .425 .840 1

Analysis of decaynmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decaynmda
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: decaynmda
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: decaynmda
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: decaynmda
transfection contrast
- -0.5
+ 0.5
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decaynmda
Source F Df Df.res Pr(>F) BF
mutation 2.96 4 15.57 .054 0.638
transfection 48.58 1 91.00 <.001 1.16e+07
mutation:transfection 8.59 4 91.00 <.001 1.08e+05
mutationWT_vs_Mutants:transfection1 19.12 1 91.00 <.001
mutationLOF_vs_GOF:transfection1 16.39 1 91.00 <.001
mutationLOF:transfection1 0.74 1 91.00 .390
mutationGOF:transfection1 0.15 1 91.00 .700
Intraclass correlation coefficients for random effects: decaynmda
Group ICC N
pair:(slice:animal) 0.000 96
slice:animal 0.010 62
animal 0.149 22
residual 0.842 192
Estimated marginal means with 95% confidence intervals: decaynmda
mutation transfection df response n lower.CL upper.CL
WT - 26.53 71.37 19 54.92 92.76
R540H - 26.94 71.65 27 57.08 89.93
R696H - 30.04 56.15 17 42.83 73.61
C456Y - 35.56 70.96 18 55.12 91.34
C461F - 25.11 99.19 15 73.66 133.57
WT + 26.53 78.86 19 60.68 102.49
R540H + 26.94 56.46 27 44.99 70.87
R696H + 30.04 41.33 17 31.52 54.19
C456Y + 35.56 34.36 18 26.69 44.23
C461F + 25.11 40.44 15 30.04 54.46
[1] “Overall average of decaynmda for untransfected neurons: 72.63”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decaynmda
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 91 1.10 0.85 1.43
(+) / (-) R540H 91 0.79 0.63 0.98
(+) / (-) R696H 91 0.74 0.56 0.97
(+) / (-) C456Y 91 0.48 0.37 0.63
(+) / (-) C461F 91 0.41 0.30 0.55
95% confidence intervals for contrasts: decaynmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 91 0.71 0.51 1.00
R696H / WT (+) / (-) 91 0.67 0.46 0.97
C456Y / WT (+) / (-) 91 0.44 0.30 0.64
C461F / WT (+) / (-) 91 0.37 0.25 0.55
Standardized effect sizes (r) for contrasts: decaynmda
mutation transfection n r 95% CI
R540H / WT (+) / (-) 96 -0.20 [-0.39, -0.00]
R696H / WT (+) / (-) 96 -0.22 [-0.40, -0.02]
C456Y / WT (+) / (-) 96 -0.42 [-0.57, -0.24]
C461F / WT (+) / (-) 96 -0.47 [-0.61, -0.30]
## Note: df set to 91
Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): decaynmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl ratio df null t.ratio p.value p.adj maxBF
R540H / WT (+) / (-) 0.71 91 1 -1.98 .051 .064 2.08
R696H / WT (+) / (-) 0.67 91 1 -2.13 .035 .064 2.08
C456Y / WT (+) / (-) 0.44 91 1 -4.40 <.001 <.001 363
C461F / WT (+) / (-) 0.37 91 1 -5.06 <.001 <.001 3.19e+03

Analysis of dt50nmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50nmda
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: dt50nmda
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: dt50nmda
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: dt50nmda
transfection contrast
- -0.5
+ 0.5
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50nmda
Source F Df Df.res Pr(>F) BF
mutation 4.86 4 15.99 .009 3.59
transfection 57.81 1 91.00 <.001 4.09e+07
mutation:transfection 10.06 4 91.00 <.001 6.05e+05
mutationWT_vs_Mutants:transfection1 21.45 1 91.00 <.001
mutationLOF_vs_GOF:transfection1 18.48 1 91.00 <.001
mutationLOF:transfection1 0.87 1 91.00 .352
mutationGOF:transfection1 1.27 1 91.00 .263
Intraclass correlation coefficients for random effects: dt50nmda
Group ICC N
pair:(slice:animal) 0.000 96
slice:animal 0.043 62
animal 0.224 22
residual 0.733 192
Estimated marginal means with 95% confidence intervals: dt50nmda
mutation transfection df response n lower.CL upper.CL
WT - 22.91 34.63 19 27.92 42.94
R540H - 24.51 32.52 27 27.04 39.11
R696H - 25.55 23.50 17 18.85 29.30
C456Y - 29.43 35.03 18 28.57 42.95
C461F - 21.89 35.99 15 28.16 46.00
WT + 22.91 36.94 19 29.79 45.80
R540H + 24.51 28.26 27 23.49 33.99
R696H + 25.55 17.78 17 14.26 22.17
C456Y + 29.43 20.34 18 16.59 24.94
C461F + 21.89 18.35 15 14.36 23.46
[1] “Overall average of dt50nmda for untransfected neurons: 31.96”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50nmda
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 91 1.07 0.89 1.28
(+) / (-) R540H 91 0.87 0.75 1.01
(+) / (-) R696H 91 0.76 0.62 0.92
(+) / (-) C456Y 91 0.58 0.48 0.70
(+) / (-) C461F 91 0.51 0.42 0.63
95% confidence intervals for contrasts: dt50nmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 91 0.81 0.64 1.03
R696H / WT (+) / (-) 91 0.71 0.54 0.92
C456Y / WT (+) / (-) 91 0.54 0.42 0.71
C461F / WT (+) / (-) 91 0.48 0.36 0.63
Standardized effect sizes (r) for contrasts: dt50nmda
mutation transfection n r 95% CI
R540H / WT (+) / (-) 96 -0.18 [-0.37, 0.02]
R696H / WT (+) / (-) 96 -0.26 [-0.44, -0.06]
C456Y / WT (+) / (-) 96 -0.44 [-0.59, -0.26]
C461F / WT (+) / (-) 96 -0.49 [-0.63, -0.32]
## Note: df set to 91
Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): dt50nmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl ratio df null t.ratio p.value p.adj maxBF
R540H / WT (+) / (-) 0.81 91 1 -1.72 .088 .088 1.72
R696H / WT (+) / (-) 0.71 91 1 -2.59 .011 .021 4.54
C456Y / WT (+) / (-) 0.54 91 1 -4.65 <.001 <.001 783
C461F / WT (+) / (-) 0.48 91 1 -5.38 <.001 <.001 1.07e+04

Analysis of chargenmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargenmda
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: chargenmda
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: chargenmda
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: chargenmda
transfection contrast
- -0.5
+ 0.5
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargenmda
Source F Df Df.res Pr(>F) BF
mutation 3.28 4 15.86 .039 2.16
transfection 43.33 1 91.00 <.001 8.24e+06
mutation:transfection 2.26 4 91.00 .069 0.856
mutationWT_vs_Mutants:transfection1 3.97 1 91.00 .049
mutationLOF_vs_GOF:transfection1 4.57 1 91.00 .035
mutationLOF:transfection1 0.50 1 91.00 .482
mutationGOF:transfection1 0.96 1 91.00 .329
Intraclass correlation coefficients for random effects: chargenmda
Group ICC N
pair:(slice:animal) 0.112 96
slice:animal 0.249 62
animal 0.224 22
residual 0.414 192
Estimated marginal means with 95% confidence intervals: chargenmda
mutation transfection df response n lower.CL upper.CL
WT - 18.28 16.39 19 8.18 32.83
R540H - 18.86 8.67 27 4.79 15.71
R696H - 20.64 3.51 17 1.72 7.15
C456Y - 23.75 11.18 18 5.79 21.62
C461F - 17.90 8.73 15 3.94 19.36
WT + 18.28 12.74 19 6.36 25.52
R540H + 18.86 4.61 27 2.54 8.34
R696H + 20.64 2.46 17 1.21 5.01
C456Y + 23.75 4.84 18 2.50 9.35
C461F + 17.90 3.01 15 1.36 6.68
[1] “Overall average of chargenmda for untransfected neurons: 8.66”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargenmda
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 91 0.78 0.51 1.18
(+) / (-) R540H 91 0.53 0.37 0.75
(+) / (-) R696H 91 0.70 0.45 1.09
(+) / (-) C456Y 91 0.43 0.28 0.66
(+) / (-) C461F 91 0.35 0.22 0.55
95% confidence intervals for contrasts: chargenmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 91 0.68 0.40 1.18
R696H / WT (+) / (-) 91 0.90 0.49 1.66
C456Y / WT (+) / (-) 91 0.56 0.31 1.01
C461F / WT (+) / (-) 91 0.44 0.24 0.83
Standardized effect sizes (r) for contrasts: chargenmda
mutation transfection n r 95% CI
R540H / WT (+) / (-) 96 -0.14 [-0.33, 0.06]
R696H / WT (+) / (-) 96 -0.04 [-0.23, 0.17]
C456Y / WT (+) / (-) 96 -0.20 [-0.38, 0.00]
C461F / WT (+) / (-) 96 -0.26 [-0.44, -0.06]
## Note: df set to 91
Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): chargenmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl ratio df null t.ratio p.value p.adj maxBF
R540H / WT (+) / (-) 0.68 91 1 -1.39 .169 .283 1.03
R696H / WT (+) / (-) 0.90 91 1 -0.33 .739 .739 1
C456Y / WT (+) / (-) 0.56 91 1 -1.94 .055 .135 1.36
C461F / WT (+) / (-) 0.44 91 1 -2.57 .012 .041 2.8

Analysis of peakampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peakampa
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: peakampa
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: peakampa
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: peakampa
transfection contrast
- -0.5
+ 0.5
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peakampa
Source F Df Df.res Pr(>F) BF
mutation 9.18 4 16.25 <.001 62
transfection 0.72 1 91.00 .399 0.186
mutation:transfection 1.20 4 91.00 .315 0.204
mutationWT_vs_Mutants:transfection1 0.19 1 91.00 .662
mutationLOF_vs_GOF:transfection1 1.32 1 91.00 .253
mutationLOF:transfection1 3.44 1 91.00 .067
mutationGOF:transfection1 0.10 1 91.00 .747
Intraclass correlation coefficients for random effects: peakampa
Group ICC N
pair:(slice:animal) 0.085 96
slice:animal 0.184 62
animal 0.339 22
residual 0.392 192
Estimated marginal means with 95% confidence intervals: peakampa
mutation transfection df response n lower.CL upper.CL
WT - 18.13 5.72 19 3.18 10.28
R540H - 19.52 18.12 27 11.02 29.79
R696H - 19.82 51.30 17 28.24 93.18
C456Y - 22.12 20.57 18 11.90 35.56
C461F - 17.72 20.77 15 10.59 40.72
WT + 18.13 5.05 19 2.81 9.08
R540H + 19.52 18.35 27 11.17 30.17
R696H + 19.82 55.69 17 30.66 101.15
C456Y + 22.12 22.42 18 12.97 38.75
C461F + 17.72 14.46 15 7.37 28.34
[1] “Overall average of peakampa for untransfected neurons: 18.67”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peakampa
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 91 0.88 0.65 1.21
(+) / (-) R540H 91 1.01 0.78 1.32
(+) / (-) R696H 91 1.09 0.78 1.51
(+) / (-) C456Y 91 1.09 0.79 1.51
(+) / (-) C461F 91 0.70 0.49 0.99
95% confidence intervals for contrasts: peakampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 91 1.15 0.76 1.73
R696H / WT (+) / (-) 91 1.23 0.78 1.94
C456Y / WT (+) / (-) 91 1.23 0.78 1.94
C461F / WT (+) / (-) 91 0.79 0.49 1.27
Standardized effect sizes (r) for contrasts: peakampa
mutation transfection n r 95% CI
R540H / WT (+) / (-) 96 0.07 [-0.13, 0.27]
R696H / WT (+) / (-) 96 0.09 [-0.11, 0.29]
C456Y / WT (+) / (-) 96 0.10 [-0.11, 0.29]
C461F / WT (+) / (-) 96 -0.10 [-0.30, 0.10]
## Note: df set to 91
Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): peakampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl ratio df null t.ratio p.value p.adj maxBF
R540H / WT (+) / (-) 1.15 91 1 0.66 .512 .712 1
R696H / WT (+) / (-) 1.23 91 1 0.89 .375 .712 1
C456Y / WT (+) / (-) 1.23 91 1 0.92 .360 .712 1
C461F / WT (+) / (-) 0.79 91 1 -1.00 .320 .712 1

Analysis of decayampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decayampa
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: decayampa
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: decayampa
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: decayampa
transfection contrast
- -0.5
+ 0.5
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decayampa
Source F Df Df.res Pr(>F) BF
mutation 2.41 4 16.5 .091 1.46
transfection 1.54 1 91.0 .217 0.408
mutation:transfection 0.95 4 91.0 .441 0.159
mutationWT_vs_Mutants:transfection1 0.01 1 91.0 .938
mutationLOF_vs_GOF:transfection1 0.00 1 91.0 .952
mutationLOF:transfection1 3.76 1 91.0 .056
mutationGOF:transfection1 0.00 1 91.0 .949
Intraclass correlation coefficients for random effects: decayampa
Group ICC N
pair:(slice:animal) 0.000 96
slice:animal 0.261 62
animal 0.454 22
residual 0.285 192
Estimated marginal means with 95% confidence intervals: decayampa
mutation transfection df response n lower.CL upper.CL
WT - 17.41 8.34 19 5.12 13.61
R540H - 18.59 6.89 27 4.57 10.37
R696H - 18.56 3.44 17 2.10 5.64
C456Y - 20.25 7.55 18 4.80 11.85
C461F - 17.24 4.57 15 2.60 8.02
WT + 17.41 7.93 19 4.86 12.93
R540H + 18.59 6.44 27 4.28 9.69
R696H + 18.56 3.24 17 1.98 5.32
C456Y + 20.25 6.13 18 3.90 9.62
C461F + 17.24 5.03 15 2.86 8.83
[1] “Overall average of decayampa for untransfected neurons: 5.84”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decayampa
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 91 0.95 0.77 1.17
(+) / (-) R540H 91 0.93 0.79 1.11
(+) / (-) R696H 91 0.94 0.76 1.17
(+) / (-) C456Y 91 0.81 0.66 1.00
(+) / (-) C461F 91 1.10 0.87 1.38
95% confidence intervals for contrasts: decayampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 91 0.98 0.75 1.28
R696H / WT (+) / (-) 91 0.99 0.74 1.34
C456Y / WT (+) / (-) 91 0.85 0.64 1.14
C461F / WT (+) / (-) 91 1.16 0.85 1.57
Standardized effect sizes (r) for contrasts: decayampa
mutation transfection n r 95% CI
R540H / WT (+) / (-) 96 -0.01 [-0.21, 0.19]
R696H / WT (+) / (-) 96 -0.01 [-0.21, 0.20]
C456Y / WT (+) / (-) 96 -0.11 [-0.31, 0.09]
C461F / WT (+) / (-) 96 0.10 [-0.10, 0.29]
## Note: df set to 91
Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): decayampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl ratio df null t.ratio p.value p.adj maxBF
R540H / WT (+) / (-) 0.98 91 1 -0.12 .902 .989 1
R696H / WT (+) / (-) 0.99 91 1 -0.05 .959 .989 1
C456Y / WT (+) / (-) 0.85 91 1 -1.07 .288 .664 1
C461F / WT (+) / (-) 1.16 91 1 0.94 .348 .666 1

Analysis of dt50ampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50ampa
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: dt50ampa
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: dt50ampa
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: dt50ampa
transfection contrast
- -0.5
+ 0.5
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50ampa
Source F Df Df.res Pr(>F) BF
mutation 3.20 4 15.93 .042 1.67
transfection 3.05 1 91.00 .084 0.704
mutation:transfection 0.31 4 91.00 .872 0.065
mutationWT_vs_Mutants:transfection1 0.89 1 91.00 .349
mutationLOF_vs_GOF:transfection1 0.04 1 91.00 .839
mutationLOF:transfection1 0.04 1 91.00 .838
mutationGOF:transfection1 0.30 1 91.00 .588
Intraclass correlation coefficients for random effects: dt50ampa
Group ICC N
pair:(slice:animal) 0.125 96
slice:animal 0.038 62
animal 0.237 22
residual 0.599 192
Estimated marginal means with 95% confidence intervals: dt50ampa
mutation transfection df response n lower.CL upper.CL
WT - 20.82 4.29 19 3.25 5.66
R540H - 22.29 3.78 27 2.98 4.79
R696H - 23.14 2.60 17 1.96 3.45
C456Y - 26.51 3.96 18 3.05 5.14
C461F - 19.89 3.12 15 2.28 4.28
WT + 20.82 4.31 19 3.27 5.69
R540H + 22.29 3.57 27 2.81 4.53
R696H + 23.14 2.28 17 1.71 3.02
C456Y + 26.51 3.47 18 2.67 4.51
C461F + 19.89 2.83 15 2.06 3.88
[1] “Overall average of dt50ampa for untransfected neurons: 3.49”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50ampa
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 91 1.01 0.82 1.24
(+) / (-) R540H 91 0.95 0.80 1.13
(+) / (-) R696H 91 0.88 0.70 1.09
(+) / (-) C456Y 91 0.88 0.71 1.08
(+) / (-) C461F 91 0.91 0.72 1.14
95% confidence intervals for contrasts: dt50ampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 91 0.94 0.72 1.23
R696H / WT (+) / (-) 91 0.87 0.65 1.18
C456Y / WT (+) / (-) 91 0.87 0.65 1.17
C461F / WT (+) / (-) 91 0.90 0.66 1.23
Standardized effect sizes (r) for contrasts: dt50ampa
mutation transfection n r 95% CI
R540H / WT (+) / (-) 96 -0.05 [-0.25, 0.15]
R696H / WT (+) / (-) 96 -0.09 [-0.29, 0.11]
C456Y / WT (+) / (-) 96 -0.10 [-0.29, 0.11]
C461F / WT (+) / (-) 96 -0.07 [-0.27, 0.13]
## Note: df set to 91
Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): dt50ampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl ratio df null t.ratio p.value p.adj maxBF
R540H / WT (+) / (-) 0.94 91 1 -0.45 .654 .767 1
R696H / WT (+) / (-) 0.87 91 1 -0.91 .366 .767 1
C456Y / WT (+) / (-) 0.87 91 1 -0.92 .360 .767 1
C461F / WT (+) / (-) 0.90 91 1 -0.67 .506 .767 1

Analysis of chargeampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargeampa
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: chargeampa
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: chargeampa
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: chargeampa
transfection contrast
- -0.5
+ 0.5
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargeampa
Source F Df Df.res Pr(>F) BF
mutation 5.96 4 15.69 .004 11
transfection 1.66 1 91.00 .200 0.237
mutation:transfection 1.88 4 91.00 .121 0.504
mutationWT_vs_Mutants:transfection1 1.87 1 91.00 .174
mutationLOF_vs_GOF:transfection1 2.82 1 91.00 .097
mutationLOF:transfection1 3.42 1 91.00 .068
mutationGOF:transfection1 0.06 1 91.00 .803
Intraclass correlation coefficients for random effects: chargeampa
Group ICC N
pair:(slice:animal) 0.172 96
slice:animal 0.149 62
animal 0.191 22
residual 0.488 192
Estimated marginal means with 95% confidence intervals: chargeampa
mutation transfection df response n lower.CL upper.CL
WT - 19.28 0.29 19 0.18 0.48
R540H - 19.74 0.82 27 0.54 1.26
R696H - 22.00 1.27 17 0.76 2.11
C456Y - 25.67 1.02 18 0.64 1.64
C461F - 18.70 0.79 15 0.45 1.39
WT + 19.28 0.33 19 0.20 0.54
R540H + 19.74 0.84 27 0.55 1.29
R696H + 22.00 1.22 17 0.74 2.03
C456Y + 25.67 0.97 18 0.60 1.55
C461F + 18.70 0.46 15 0.26 0.81
[1] “Overall average of chargeampa for untransfected neurons: 0.76”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargeampa
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 91 1.12 0.80 1.57
(+) / (-) R540H 91 1.02 0.77 1.36
(+) / (-) R696H 91 0.97 0.67 1.38
(+) / (-) C456Y 91 0.94 0.67 1.34
(+) / (-) C461F 91 0.58 0.40 0.85
95% confidence intervals for contrasts: chargeampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 91 0.92 0.59 1.43
R696H / WT (+) / (-) 91 0.86 0.53 1.42
C456Y / WT (+) / (-) 91 0.85 0.52 1.37
C461F / WT (+) / (-) 91 0.52 0.31 0.87
Standardized effect sizes (r) for contrasts: chargeampa
mutation transfection n r 95% CI
R540H / WT (+) / (-) 96 -0.04 [-0.24, 0.16]
R696H / WT (+) / (-) 96 -0.06 [-0.26, 0.14]
C456Y / WT (+) / (-) 96 -0.07 [-0.27, 0.13]
C461F / WT (+) / (-) 96 -0.26 [-0.43, -0.06]
## Note: df set to 91
Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): chargeampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl ratio df null t.ratio p.value p.adj maxBF
R540H / WT (+) / (-) 0.92 91 1 -0.39 .695 .831 1
R696H / WT (+) / (-) 0.86 91 1 -0.58 .560 .831 1
C456Y / WT (+) / (-) 0.85 91 1 -0.68 .495 .831 1
C461F / WT (+) / (-) 0.52 91 1 -2.52 .013 .046 2.6